Advertisement

NOTICE: We are experiencing technical issues with Academy members trying to log into the JAND site using Academy member login credentials. We are working to resolve the issue as soon as possible. Alternatively, if you are an Academy member, you can access the JAND site by registering for an Elsevier account and claiming access using the links at the top of the JAND site. Email us at [email protected] for assistance. Thanks for your patience!

Effects of Bite Count Feedback from a Wearable Device and Goal Setting on Consumption in Young Adults

      Abstract

      Background

      New technologies are emerging that may help individuals engage in healthier eating behaviors. One paradigm to test the efficacy of a technology is to determine its effect relative to environment cues that are known to cause individuals to overeat.

      Objective

      The purpose of this work was to independently investigate two questions: How does the presence of a technology that provides bite count feedback alter eating behavior? and, How does the presence of a technology that provides bite count feedback paired with a goal alter eating behavior?

      Design

      Two studies investigated these research questions. The first study tested the effects of a large and small plate crossed with the presence or absence of a device that provided bite count feedback on intake. The second study tested the effects of a bite count goal with bite count feedback, again crossed with plate size, on intake. Both studies used a 2×2 between-subjects design.

      Participants/setting

      In the first study, 94 subjects (62 women aged 19.0±1.6 years with body mass index [BMI] 23.04±3.6) consumed lunch in a laboratory. The second study examined 99 subjects (56 women aged 18.5±1.5 years with BMI 22.73±2.70) under the same conditions.

      Intervention

      In both studies subjects consumed a single-course meal, using either a small or large plate. In the first study participants either wore or did not wear an automated bite counting device. In the second study all participants wore the bite counting device and were given either a low bite count goal (12 bites) or a high bite count goal (22 bites).

      Statistical analyses

      Effect of plate size, feedback, and goal on consumption (grams) and number of bites taken were assessed using 2×2 analyses of variance. As adjunct measures, the effects of serving size, bite size (grams per bite), postmeal satiety, and satiety change were also assessed.

      Results

      In the first study there was a main effect of plate size on grams consumed and number of bites taken such that eating from a large plate led to greater consumption (P=0.001) and a greater number of bites (P=0.001). There was also a main effect of feedback on consumption and number of bites taken such that those who received feedback consumed less (P=0.011) and took fewer bites (P<0.001). In the second study there was a main effect of plate size on consumption such that those eating from a large plate consumed more (P=0.003) but did not take more bites. Further analysis revealed a main effect of goal on number of bites taken such that those who received the low goal took fewer bites (P<0.001) but did not consume less.

      Conclusions

      Providing feedback on the number of bites taken from a wearable intake monitor can reduce overall intake during a single meal. Regarding the first research question, providing feedback significantly reduced intake in both plate size groups and reduced the overall number of bites taken. Regarding the second research question, participants were successful in eating to their goals. However, individuals in the low goal condition appeared to compensate for the restricted goal by taking larger bites, leading to comparable levels of consumption between the low and high goal groups. Hence, the interaction of technology with goals should be considered when introducing a health intervention.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the Academy of Nutrition and Dietetics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Flegal K.M.
        • Carroll M.D.
        • Ogden C.L.
        • Curtin L.R.
        Prevalence and trends in obesity among US adults, 1999-2008.
        JAMA. 2010; 303: 235-241
        • Wansink B.
        From mindless eating to mindlessly eating better.
        Physiol Behav. 2005; 100: 454-463
        • Wansink B.
        • Kim J.
        Bad popcorn in big buckets: Portion size can influence intake as much as taste.
        J Nutr Educ Behav. 2005; 37: 242-245
        • Wansink B.
        • Cheney M.M.
        Super bowls: Serving bowl size and food consumption.
        JAMA. 2005; 293: 1723-1728
        • Wansink B.
        • Van Ittersum K.
        • Painter J.E.
        Ice cream illusions: Bowls, spoons, and self-served portion sizes.
        Am J Prev Med. 2006; 31: 240-243
      1. Hapifork [package insert]. HapiLabs, Hong Kong, China.

      2. Mandometer [package insert]. Mandometer. Brighton, Victoria, Australia.

        • Burke L.E.
        • Wang J.
        • Sevick M.A.
        Self-monitoring in weight loss: A systematic review of the literature.
        J Am Diet Assoc. 2011; 111: 92-102
        • Bravata D.M.
        • Smith-Spangler C.
        • Sundaram V.
        • Gienger A.L.
        • Lin N.
        • Lewis R.
        • Sirard J.R.
        Using pedometers to increase physical activity and improve health.
        JAMA. 2007; 298: 2296-2304
        • Dong Y.
        • Hoover A.
        • Scisco J.
        • Muth E.
        A new method for measuring meal intake in humans via automated wrist motion tracking.
        Appl Psychophyisol Biofeedback. 2012; 37: 205-215
      3. Scisco JL. Source of Variance in Bite Count. PhD dissertation. Clemson, SC: Clemson University Psychology Department; 2012.

        • Baker R.C.
        • Kirschenbaum D.S.
        Self-monitoring may be necessary for successful weight control.
        Behav Ther. 1993; 24: 377-394
        • Bandura A.
        • Cervone D.
        Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems.
        J Pers Soc Psychol. 1983; 45: 1017
        • Dupont W.D.
        • Plummer W.D.
        PS: Power and sample size calculation.
        Control Clin Trials. 1990; 11: 116-128
        • Cardello A.V.
        • Schutz H.
        • Snow C.
        • Lesher L.
        Predictors of food acceptance, consumption and satisfaction in specific eating situations.
        Food Qual Pref. 2000; 11: 201-216
        • Salvy S.
        • Kieffer E.
        • Epstein L.
        Effects of social context on overweight and normal-weight children’s food selection.
        Eat Behav. 2008; 9: 190-196
      4. IBM-SPSS Statistics for Windows, version 20.0. Released 2011. Armonk, NY: IBM Corp.

        • Wansink B.
        • Payne C.R.
        Counting bones: Environmental cues that decrease food intake.
        Percept Motor Skills. 2007; 104: 273-276
        • Bandura A.
        • Simon K.M.
        The role of proximal intentions in self-regulation of refractory behavior.
        Cognit Ther Res. 1977; 1: 177-193
        • Chandon P.
        • Wansink B.
        The biasing health halos of fast-food restaurant health claims: Lower calorie estimates and higher side-dish consumption intentions.
        J Consum Res. 2007; 34: 301-314
        • Guss J.L.
        • Kissileff H.R.
        Microstructural analyses of human ingestive patterns: From description to mechanistic hypotheses.
        Neurosci Biobehav Rev. 2000; 24: 261-268
        • Guo F.
        • Li Y.
        • Kankanhalli M.
        • Brown M.
        An evaluation of wearable activity monitoring devices.
        in: Proceedings of the 1st ACM International Workshop on Personal Data Meets Distributed Multimedia. Association for Computing Machinery, New York, NY2013

      Biography

      P. W. Jasper is a research assistant, Department of Psychology, Clemson University, Clemson, SC.

      Biography

      E. R. Muth is a professor, Department of Psychology, Clemson University, Clemson, SC.

      Biography

      M. T. James is a graduate assistant, School of Computing, Clemson University, Clemson, SC.

      Biography

      A. W. Hoover is an associate professor, Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC.